Calibrating Rail Transit Assignment Models with Genetic Algorithm and Automated Fare Collection Data

Developments in the application of automatic data collection (ADC) such as automated fare collection (AFC) systems have made the collection of detailed passenger trip data in an urban rail transit (URT) network possible. AFC systems using smart card technology have become the main method for collecting urban rail transit (URT) fares in many cities around the world. The transaction data obtained through these AFC systems contain a large amount of archived information including how passengers use the URT system. The information obtained from AFC systems can be used in calibrating assignment models for precise passenger flow calculation. This paper presents a methodology for calibrating URT assignment models using AFC data. The study provides an approach that calibrates models disaggregately based on AFC data that avoids some disadvantages of traditional manual data collection approaches and can be incorporated into an automatic calibration procedure for easily obtaining accurate results.

• Record URL:
  https://doi.org/10.1111/mice.12075
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/10939687
• Authors:
  • Zhu, Wei
  • Hu, Hao
  • Huang, Zhaodong
• Publication Date: 2014-8

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 518-530
• Serial:
  • Computer-Aided Civil and Infrastructure Engineering
  • Volume: 29
  • Issue Number: 7
  • Publisher: Blackwell Publishing
  • ISSN: 1093-9687
  • Serial URL: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1467-8667

Subject/Index Terms

• TRT Terms: Algorithms; Automatic data collection systems; Automatic fare collection; Calibration; Passenger traffic; Rail transit; Smart cards; Traffic assignment; Urban transit
• Subject Areas: Data and Information Technology; Planning and Forecasting; Railroads; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01536376
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 28 2014 9:12AM